from CollectiveMotion import CollectiveMotionClass as CMClass
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
cm = CMClass()
blocks = 2**0
block_size = 2**9
n = blocks * block_size
timestep = 0.05
ra = 0.8
rb = 0.2
re = 0.5
r0 = 1.0
b = 5.0
J = 0.1
def update():
global pos, vel, K
n0 = np.random.randn(n, 3)
n0 *= 1.0 / np.linalg.norm(n0, axis=1).reshape(-1, 1)
n0 = n0.astype(np.float32)
pos, vel = cm.step(pos, vel, n, timestep, ra, rb, re, r0, b, J, blocks,
block_size)
pos -= np.mean(pos, axis=0)
from CollectiveMotion import CollectiveMotionClass as CMClass
from CollectiveMotionFunctions import fisher
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
nc = 20
cm = CMClass()
cm.set_nc(20)
blocks = 2**0
block_size = 2**9
n = blocks * block_size
timestep = 0.05
ra = 0.8
rb = 0.2
re = 0.5
r0 = 1.0
b = 5.0
J = 0.1
def update():
global pos, vel, K
n0 = np.random.randn(n,3)
n0 *= 1.0 / np.linalg.norm(n0, axis=1).reshape(-1,1)
n0 = n0.astype(np.float32)
pos, vel = cm.step(pos, vel, n, timestep,
from CollectiveMotion import CollectiveMotionClass as CMClass
from CollectiveMotionFunctions import get_probability_i
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
nc = 20
cm = CMClass()
cm.set_nc(nc)
blocks = 2**0
block_size = 2**9
n = blocks * block_size
timestep = 0.05
ra = 0.8
rb = 0.2
re = 0.5
r0 = 1.0
b = 5.0
J = 0.1
def update():
global pos, vel, K
n0 = np.random.randn(n,3)
n0 *= 1.0 / np.linalg.norm(n0, axis=1).reshape(-1,1)
n0 = n0.astype(np.float32)
pos, vel = cm.step(pos, vel, n, timestep,